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52 changes: 52 additions & 0 deletions docs/build/AffineInvariantMCMC.md
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<a id='AffineInvariantMCMC.jl-1'></a>

# AffineInvariantMCMC.jl


Module AffineInvariantMCMC.jl provides functions for Bayesian sampling using Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler (aka Emcee) based on a paper by Goodman & Weare, "Ensemble samplers with affine invariance" Communications in Applied Mathematics and Computational Science, DOI: [10.2140/camcos.2010.5.65](http://dx.doi.org/10.2140/camcos.2010.5.65), 2010.


AffineInvariantMCMC.jl module functions:

<a id='AffineInvariantMCMC.flattenmcmcarray-Tuple{Array,Array}' href='#AffineInvariantMCMC.flattenmcmcarray-Tuple{Array,Array}'>#</a>
**`AffineInvariantMCMC.flattenmcmcarray`** &mdash; *Method*.



Flatten MCMC arrays


<a target='_blank' href='https://github.com/madsjulia/AffineInvariantMCMC.jl/tree/104df63a5b9de2991793e1f99fc117f037b72357/src/AffineInvariantMCMC.jl#L96' class='documenter-source'>source</a><br>

<a id='AffineInvariantMCMC.sample' href='#AffineInvariantMCMC.sample'>#</a>
**`AffineInvariantMCMC.sample`** &mdash; *Function*.



Bayesian sampling using Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler (aka Emcee)

```
AffineInvariantMCMC.sample(llhood, numwalkers=10, numsamples_perwalker=100, thinning=1)
```

Arguments:

* `llhood` : function estimating loglikelihood (for example, generated using Mads.makearrayloglikelihood())
* `numwalkers` : number of walkers
* `x0` : normalized initial parameters (matrix of size (length(params), numwalkers))
* `thinning` : removal of any `thinning` realization
* `a` :

Returns:

* `mcmcchain` : final MCMC chain
* `llhoodvals` : log likelihoods of the final samples in the chain

Reference:

Goodman & Weare, "Ensemble samplers with affine invariance", Communications in Applied Mathematics and Computational Science, DOI: 10.2140/camcos.2010.5.65, 2010.


<a target='_blank' href='https://github.com/madsjulia/AffineInvariantMCMC.jl/tree/104df63a5b9de2991793e1f99fc117f037b72357/src/AffineInvariantMCMC.jl#L36-L59' class='documenter-source'>source</a><br>

79,403 changes: 79,402 additions & 1 deletion docs/build/Anasol.md

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76 changes: 76 additions & 0 deletions docs/build/BIGUQ.md
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<a id='BIGUQ.jl-1'></a>

# BIGUQ.jl


Module BIGUQ provides advanced techniques for Uncertainty Quantification, Experimental Design and Decision Analysis based on Bayesian Information Gap Decision Theory (BIGDT).


References:


* O’Malley, D., Vesselinov, V.V., A combined probabilistic/non-probabilistic decision analysis for contaminant remediation, Journal on Uncertainty Quantification, SIAM/ASA, 10.1137/140965132, 2014.
* O’Malley, D., Vesselinov, V.V., Bayesian-Information-Gap decision theory with an application to CO2 sequestration, Water Resources Research, 10.1002/2015WR017413, 2015.
* Grasinger, M., O'Malley, D., Vesselinov, V.V., Karra, S., Decision Analysis for Robust CO2 Injection: Application of Bayesian-Information-Gap Decision Theory, International Journal of Greenhouse Gas Control, 10.1016/j.ijggc.2016.02.017, 2016.


Relevant examples:


* [Information Gap Analysis](http://madsjulia.github.io/Mads.jl/Examples/infogap)
* [Decision Analysis](http://madsjulia.github.io/Mads.jl/Examples/bigdt/source_termination)


BIGUQ.jl module functions:

<a id='BIGUQ.getmcmcchain-Tuple{BIGUQ.BigDT,Any}' href='#BIGUQ.getmcmcchain-Tuple{BIGUQ.BigDT,Any}'>#</a>
**`BIGUQ.getmcmcchain`** &mdash; *Method*.



Get MCMC chain


<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGDT.jl#L22' class='documenter-source'>source</a><br>

<a id='BIGUQ.makebigdts-Tuple{BIGUQ.BigOED,Any,Any}' href='#BIGUQ.makebigdts-Tuple{BIGUQ.BigOED,Any,Any}'>#</a>
**`BIGUQ.makebigdts`** &mdash; *Method*.



Make BIGDT analyses for each possible decision assuming that the proposed observations `proposedobs` are observed


<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L50' class='documenter-source'>source</a><br>

<a id='BIGUQ.makebigdts-Tuple{BIGUQ.BigOED}' href='#BIGUQ.makebigdts-Tuple{BIGUQ.BigOED}'>#</a>
**`BIGUQ.makebigdts`** &mdash; *Method*.



Makes BIGDT analyses for each possible decision assuming that no more observations will be made


<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L23' class='documenter-source'>source</a><br>

<a id='BIGUQ.BigDT' href='#BIGUQ.BigDT'>#</a>
**`BIGUQ.BigDT`** &mdash; *Type*.



BigOED type


<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGDT.jl#L2' class='documenter-source'>source</a><br>

<a id='BIGUQ.BigOED' href='#BIGUQ.BigOED'>#</a>
**`BIGUQ.BigOED`** &mdash; *Type*.



BigOED type


<a target='_blank' href='https://github.com/madsjulia/BIGUQ.jl/tree/770772d2a5e0c0945e40430564fa787fe9b47398/src/BIGOED.jl#L1' class='documenter-source'>source</a><br>

100 changes: 100 additions & 0 deletions docs/build/DocumentFunction.md
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<a id='DocumentFunction.jl-1'></a>

# DocumentFunction.jl


Module provides tools for documenting Julia functions providing information about function methods, arguments and keywords.


DocumentFunction.jl module functions:

<a id='DocumentFunction.documentfunction' href='#DocumentFunction.documentfunction'>#</a>
**`DocumentFunction.documentfunction`** &mdash; *Function*.



Create function documentation

Arguments:

* `f`: function to be documented"

Keywords:

* `maintext`: function description
* `argtext`: dictionary with text for each argument
* `keytext`: dictionary with text for each keyword
* `location`: show/hide function location on the disk


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L109-L122' class='documenter-source'>source</a><br>

<a id='DocumentFunction.getfunctionarguments' href='#DocumentFunction.getfunctionarguments'>#</a>
**`DocumentFunction.getfunctionarguments`** &mdash; *Function*.



Get function arguments

Arguments:

* `f`: function to be documented"
* `m`: function methods


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L147-L154' class='documenter-source'>source</a><br>

<a id='DocumentFunction.getfunctionkeywords' href='#DocumentFunction.getfunctionkeywords'>#</a>
**`DocumentFunction.getfunctionkeywords`** &mdash; *Function*.



Get function keywords

Arguments:

* `f`: function to be documented
* `m`: function methods


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L177-L184' class='documenter-source'>source</a><br>

<a id='DocumentFunction.getfunctionmethods-Tuple{Function}' href='#DocumentFunction.getfunctionmethods-Tuple{Function}'>#</a>
**`DocumentFunction.getfunctionmethods`** &mdash; *Method*.



Get function methods

Arguments:

* `f`: function to be documented

Return:

* array with function methods


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L37-L47' class='documenter-source'>source</a><br>

<a id='DocumentFunction.stdoutcaptureoff-Tuple{}' href='#DocumentFunction.stdoutcaptureoff-Tuple{}'>#</a>
**`DocumentFunction.stdoutcaptureoff`** &mdash; *Method*.



Restore STDOUT


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L26-L28' class='documenter-source'>source</a><br>

<a id='DocumentFunction.stdoutcaptureon-Tuple{}' href='#DocumentFunction.stdoutcaptureon-Tuple{}'>#</a>
**`DocumentFunction.stdoutcaptureon`** &mdash; *Method*.



Redirect STDOUT to a reader


<a target='_blank' href='https://github.com/madsjulia/DocumentFunction.jl/tree/d3db0c920e37b46a8ebac13fa09aa0f961f18399/src/DocumentFunction.jl#L15-L17' class='documenter-source'>source</a><br>

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